Variation in in situ chlorophyll-a, TSM, and SDD. Points represent individual sampling stations. Open circles represent field sampling values during LE7 overpass. Closed circles represent field sampling values during LC8 overpass

Variation in in situ chlorophyll-a, TSM, and SDD. Points represent individual sampling stations. Open circles represent field sampling values during LE7 overpass. Closed circles represent field sampling values during LC8 overpass

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The launch of the Landsat 8 in February 2013 extended the life of the Landsat program to over 40 years, increasing the value of using Landsat to monitor long-term changes in the water quality of small lakes and reservoirs, particularly in poorly monitored freshwater systems. Landsat-based water quality hindcasting often incorporate several Landsat...

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... Remote sensing applications, as the one presented here, may be useful to expand the scope of drought impacts and quality assessments within open water bodies across wide areas. Advances in public sensor technology and cloud-computing environments nowadays offer notable benefits in more reliable and cost-efficient assessments of water quality, with indices such as band ratios and more specific ones such as the Normalized Difference Chlorophyll Index (NDCI) potentially enhancing our understanding of dynamic changes and anomalies in aquatic ecosystems (Deutsch et al., 2018;Mishra and Mishra, 2012). Besides, our modelling procedure can support initiatives aimed at restoring hydrological cycles (De Groot et al., 2018;Gleick, 1998), improving renewable water resources in regions lacking significant reservoirs, and promoting the planning of supplementary or alternative water bodies (Erwin, 2009;Morante-Carballo et al., 2022). ...
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... Hence, understanding measurement stability (electronic drift) or fouling rates (devices lack an automated cleaning system) remains limited (Delgado et al., 2021). Further, there has been a lack of multiple devices fabricated to enable cross-comparisons between sensors to quantify inter-sensor measurement uncertainty (Deutsch et al., 2018;Fettweis et al., 2019). Also, challenges associated with the calibration of multi-node sensor networks (lower-cost sensors will potentially facilitate more extensive networks of optical sensors) and best practices for using calibration reference standards, mainly when sensors are deployed in remote locations, are poorly defined (Earp et al., 2011). ...
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... Due to its global perspective, these satellite sensors usually have a spatial resolution ranging from 100 m to 1 km, thus inadequate for the monitoring of nearshore and inland waters, where red tides or oil spills are usually patchy and on a scale of 10s meters. To take advantage of the 30-m resolution offered by the Landsat TM or ETM images, there are many studies using data from these sensors to monitor water qualities of small water bodies or those with fine features [8], [9], [10]. The limited spectral bands and its low signal-to-noise ratio (SNR), however, make such sensors far from ideal for monitoring the aquatic ecosystem of nearshore or inland waters. ...
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... The Landsat program has been widely used to monitor optically active water characteristics, such as Chl-a, TSS, and water clarity (Al-Fahdawi et al., 2015;Chen et al., 2020;Dave et al., 2019;Deutsch et al., 2018;Forghani et al., 2021;Jaelani et al., 2016;Markogianni et al., 2014;Olmanson et al., 2008;Olmanson et al., 2016;Owusu et al., 2019;Rodríguez et al., 2014;Sun et al., 2015;Papenfus et al., 2020). Meanwhile, several studies have used the Sentinel 2 MSI to retrieve optically active water quality parameters from freshwater bodies, in particular, Chl-a (Caballero & Navarro, 2021;Grendaitė et al., 2018;Hussein & Assaf, 2020;Karaoui et al., 2019;Lobo et al., 2021;Nguyen et al., 2021;Pantoja et al., 2021;Pinardi et al., 2018;Poddar et al., 2019;Pompeo et al., 2021;Potes et al., 2018;Tavares et al., 2021;Toming et al., 2016;Tóth et al., 2021;Warren et al., 2021;Abdelal et al., 2022), TSS (Caballero & Navarro, 2021;Hussein et al., 2023;Liu et al., 2017;Tham et al., 2021), PC , SDD (Boldanova, 2021;Bonansea et al., 2019;Pompeo et al., 2021;Rodrigues et al., 2020;, and colored dissolved organic matter (CDOM) Toming et al., 2016). ...
... Fadel et al. (2016) assessed the potential of using Landsat 8 to assess Chl-a concentrations in the reservoir. Meanwhile, Deutsch et al. (2018) assessed the potential of Landsat 7 and 8 to predict, Chla-a, TSM, and SDD in the reservoir. Recently, Sharaf et al. (2019) proposed a PC model that was based on Landsat 8 reflectance data. ...
... Note that even though water quality may fluctuate within the defined 30 m buffer area, we assumed that the in situ measurements represented average conditions within their direct neighborhood. This is a common assumption used in most remote sensing-based water quality studies (Bonansea et al., 2015;Cheng & Lei, 2001;Deutsch et al., 2018). In total, 113 in situ samples were used to develop the Chl-a models. ...
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... These were used separately as the dataset for model training. Several studies have used empirical algorithms with single-band and band combinations (or dual-band and band combinations) [10,30,36,[38][39][40]. The results of these studies show that using band combinations for remote-sensing inversion of lake water quality is better than using single bands [41]. ...
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... The reservoir is located in an area that is characterized by hot dry summers and moderately cold wet winters-typical of the Mediterranean region. Annual precipitation rates in the area range between 250 and 750 mm, with an average of 600 mm per year, mostly falling between December and March (Deutsch et al. 2018;Geara-Matta et al. 2010). The reservoir is subject to point and non-point pollutant loads that reach it from agricultural, industrial, and domestic wastewater discharges. ...
... The reservoir is subject to point and non-point pollutant loads that reach it from agricultural, industrial, and domestic wastewater discharges. These discharges have severely deteriorated the reservoir's water quality, limiting its ability to meet its designated uses (BAMAS 2005;Darwish et al. 2021;Deutsch et al. 2018;ELARD 2011;Fadel et al. 2021;Fadel et al. 2019;Jurdi et al. 2002;Shaban and Hamzé 2018;Sharaf et al. 2019). Given that the reservoir is located in a semi-arid hot climate, it has a high propensity to suffer from algal blooms (Shaban and Hamzé 2018). ...
... Nevertheless, the fact that the Chl-a based TSI values were higher than those based on SDD indicates that algae, in particular, large algal particulates, dominated the attenuation of light in the reservoir's water column (Carlson and Simpson 1996). This confirms that most of the Qaraoun's turbidity is a direct result of excessive algal growth, as previously reported by Deutsch et al. (2018) and Fadel et al. (2016). The random forests model fared well in predicting the trophic state in Qaraoun Reservoir. ...
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Excessive point and non-point nutrient loadings accompanied with elevated temperatures have increased the prevalence of harmful algal bloom (HAB). HABs pose significant environmental and public health concerns, particularly for inland freshwater systems. In this study, the eutrophication and HAB dynamics in the Qaraoun Reservoir, a hypereutrophic deep monomictic reservoir suffering from poor water quality, were assessed. The reservoir was mostly phosphorus limited, and large algal particulates dominated light attenuation in the water column. During bloom events, surface chlorophyll-a concentrations increased up to 961.3 µg/L, while surface concentrations of ammonia and ortho-phosphate were rapidly depleted; surface dissolved oxygen reached supersaturation levels and surface pH levels were up to 3 units higher than those measured in the hypolimnion. Meanwhile, measured Microcystin-LR toxin concentrations in the reservoir exceeded the World Health Organization 1 μg/L provisional guideline 45% of the times. Yet, the results showed that most of the toxins were intra-cellular, suggesting that they decayed rapidly when released into the reservoir. Results from a random forests ensemble model indicated that tracking the changes in surface dissolved oxygen levels, ammonium, ortho-phosphate, and pH can be an effective program towards predicting the reservoir’s trophic state and algae blooms.
... The decrease of dissolved oxygen (DO) and the increase of total nitrogen (TN) and total phosphorus (TP) are the important signs of water quality deterioration in middle of northeast China. Because the optical properties of TN and TP in waterbodies are not obvious, and standard physical models cannot be used to construct their concentration estimation equations based on remote sensing [15], some empirical models based on spectral combination have been proposed and developed to predict water quality parameters [16][17][18]. The spectral characteristics of the waterbody are mainly influenced by the Chl-a concentration [19], total suspended sediment (TSS) [20], suspended particulate matter (SPM) [10], and other factors (e.g., underwater topography and water depth). ...
... The studies found when Chl-a concentration of >5 µg/L, the spectral reflectance ratio (R705/R675) of the studied waterbody and the position of the Chl-a reflection peak near 700 nm had a good correlation with the Chl-a concentration [21]. It is also found that suspended material (SPM), such as sediment and algae, scatter and reflect the light entering the waterbody, thus improving the reflectivity of the waterbody [16]. The spectral attenuation characteristics of a waterbody vary with pollutant concentrations [21,22]. ...
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To estimate the key water quality parameters on a large scale, based on Pearson’s correlation analysis and band ratio, this study first obtains multiple sensitive band combinations (R ≥ 0.30, p < 0.01) for three key water quality parameters: dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Then, principal component analysis is used to reduce the dimensions and analyze multiple optimal combinations, and the first three principal components (PCs) of the optimal combinations are selected to analyze the water quality parameters. Finally, the water quality parameter models of DO, TN, and TP are proposed and compared based on spectral analysis and field measured water quality data respectively using Gaussian process regression and PCs for each parameter. Through model verification and by comparing the performance of the three models, it is found that the TP model performed well (R = 0.9824, p < 0.01), and TP grade accuracy rate is up to 94.97%. Through the error analysis of TN and DO, it is found that 93.0% of error samples occurs when TP < 0.1 mg/L in the water quality. These results would provide a scientific basis for water quality monitoring and water environment management in the study area and could also be used as a reference for water quality monitoring in other basins.